The 2019-2020 Coronavirus Pandemic Analysis

Contact: Smith Research

BACKGROUND & APPROACH

I wanted to track and trend the coronavirus outbreak on my own curiosity. There are some interesting questions that may fall out of this, as it is a very historic moment, including scientifically and analytically (we have a large amount of data being shared across the globe, analyzed in real-time). The world has come to a halt because of it.
This analysis attempts to answer the following questions (more to come):

  1. What does the trend of the pandemic look like to date?
  2. What are future case predictions based on historical model?
  3. What interesting quirks or patterns emerge?

ASSUMPTIONS & LIMITATIONS: * This data is limited by the source. I realized early on that depending on source there were conflicting # of cases. Originally I was using JHU data… but this was always ‘ahead’ of the Our World In Data. I noticed that JHU’s website was buggy- you clicked on the U.S. stats but it didn’t reflect the U.S.. So I changed data sources to be more consistent with what is presented in the media (and Our World In Data has more extensive plots I can compare my own to). An interesting aside might be why the discrepancy? Was I missing something?
* Defintiions are important as is the idea that multiple varibales accumulate in things like total cases (more testing for example).

SOURCE RAW DATA: * https://ourworldindata.org/coronavirus
* https://github.com/CSSEGISandData/COVID-19/
*

INPUT DATA LOCATION: github (https://github.com/sbs87/coronavirus/tree/master/data)

OUTPUT DATA LOCATIOn: github (https://github.com/sbs87/coronavirus/tree/master/results)

TIMESTAMP

Start: ##—— Wed May 27 21:57:37 2020 ——##

PRE-ANALYSIS

The following sections are outside the scope of the ‘analysis’ but are still needed to prepare everything

UPSTREAM PROCESSING/ANALYSIS

  1. Google Mobility Scraping, script available at get_google_mobility.py
# Mobility data has to be extracted from Google PDF reports using a web scraping script (python , written by Peter Simone, https://github.com/petersim1/MIT_COVID19)

# See get_google_mobility.py for local script 

python3 get_google_mobility.py
# writes csv file of mobility data as "mobility.csv"

SET UP ENVIORNMENT

Load libraries and set global variables

# timestamp start
timestamp()
## ##------ Wed May 27 21:57:37 2020 ------##

# clear previous enviornment
rm(list = ls())

##------------------------------------------
## LIBRARIES
##------------------------------------------
library(plyr)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.0     ✓ purrr   0.3.3
## ✓ tibble  3.0.0     ✓ dplyr   0.8.5
## ✓ tidyr   1.0.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::arrange()   masks plyr::arrange()
## x purrr::compact()   masks plyr::compact()
## x dplyr::count()     masks plyr::count()
## x dplyr::failwith()  masks plyr::failwith()
## x dplyr::filter()    masks stats::filter()
## x dplyr::id()        masks plyr::id()
## x dplyr::lag()       masks stats::lag()
## x dplyr::mutate()    masks plyr::mutate()
## x dplyr::rename()    masks plyr::rename()
## x dplyr::summarise() masks plyr::summarise()
## x dplyr::summarize() masks plyr::summarize()
library(ggplot2)
library(reshape2)
## 
## Attaching package: 'reshape2'
## The following object is masked from 'package:tidyr':
## 
##     smiths
library(plot.utils)
library(utils)
library(knitr)

##------------------------------------------

##------------------------------------------
# GLOBAL VARIABLES
##------------------------------------------
user_name <- Sys.info()["user"]
working_dir <- paste0("/Users/", user_name, "/Projects/coronavirus/")  # don't forget trailing /
results_dir <- paste0(working_dir, "results/")  # assumes diretory exists
results_dir_custom <- paste0(results_dir, "custom/")  # assumes diretory exists


Corona_Cases.source_url <- "https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
Corona_Cases.US.source_url <- "https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv"
Corona_Deaths.US.source_url <- "https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_US.csv"
Corona_Deaths.source_url <- "https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv"

Corona_Cases.fn <- paste0(working_dir, "data/", basename(Corona_Cases.source_url))
Corona_Cases.US.fn <- paste0(working_dir, "data/", basename(Corona_Cases.US.source_url))
Corona_Deaths.fn <- paste0(working_dir, "data/", basename(Corona_Deaths.source_url))
Corona_Deaths.US.fn <- paste0(working_dir, "data/", basename(Corona_Deaths.US.source_url))
default_theme <- theme_bw() + theme(text = element_text(size = 14))  # fix this
##------------------------------------------

FUNCTIONS

List of functions

function_name description
prediction_model outputs case estumate for given log-linear moder parameters slope and intercept
make_long converts input data to long format (specialized cases)
name_overlaps outputs the column names intersection and set diffs of two data frame
find_linear_index finds the first date at which linearaity occurs 
##------------------------------------------
## FUNCTION: prediction_model
##------------------------------------------
## --- //// ----
# Takes days vs log10 (case) linear model parameters and a set of days since 100 cases and outputs a dataframe with total number of predicted cases for those days
## --- //// ----
prediction_model<-function(m=1,b=0,days=1){
  total_cases<-m*days+b
  total_cases.log<-log(total_cases,10)
  prediction<-data.frame(days=days,Total_confirmed_cases_perstate=total_cases)
  return(prediction)
}
##------------------------------------------

##------------------------------------------
## FUNCTION: make_long
##------------------------------------------
## --- //// ----
# Takes wide-format case data and converts into long format, using date and total cases as variable/values. Also enforces standardization/assumes data struture naming by using fixed variable name, value name, id.vars, 
## --- //// ----
make_long<-function(data_in,variable.name = "Date",
                   value.name = "Total_confirmed_cases",
                   id.vars=c("case_type","Province.State","Country.Region","Lat","Long","City","Population")){

long_data<-melt(data_in,
                id.vars = id.vars,
                variable.name=variable.name,
                value.name=value.name)
return(long_data)

}
##------------------------------------------

## THIS WILL BE IN UTILS AT SOME POINT
name_overlaps<-function(df1,df2){
i<-intersect(names(df1),
names(df2))
sd1<-setdiff(names(df1),
names(df2))
sd2<-setdiff(names(df2),names(df1))
cat("intersection:\n",paste(i,"\n"))
cat("in df1 but not df2:\n",paste(sd1,"\n"))
cat("in df2 but not df1:\n",paste(sd2,"\n"))
return(list("int"=i,"sd_1_2"=sd1,"sd_2_1"=sd2))
}

##------------------------------------------

##------------------------------------------
## FUNCTION: find_linear_index
##------------------------------------------
## --- //// ----
# Find date at which total case data is linear (for a given data frame) 
## --- //// ----

find_linear_index<-function(tmp,running_avg=5){
  tmp$Total_confirmed_cases_perstate.log<-log(tmp$Total_confirmed_cases_perstate,2)
  derivative<-data.frame(matrix(nrow = nrow(tmp),ncol = 4))
  names(derivative)<-c("m.time","mm.time","cumsum","date")
  
  # First derivative
  for(t in 2:nrow(tmp)){
    slope.t<- tmp[t,"Total_confirmed_cases_perstate.log"]- tmp[t-1,"Total_confirmed_cases_perstate.log"]
    derivative[t,"m.time"]<-slope.t
    derivative[t,"date"]<-as.Date(tmp[t,"Date"])
  }
  
  # Second derivative
  for(t in 2:nrow(derivative)){
    slope.t<- derivative[t,"m.time"]- derivative[t-1,"m.time"]
    derivative[t,"mm.time"]<-slope.t
  }
  
  #Compute running sum of second derivative (window = 5). Choose point at which within 0.2
  for(t in running_avg:nrow(derivative)){
    slope.t<- sum(abs(derivative[t:(t-4),"mm.time"])<0.2,na.rm = T)
    derivative[t,"cumsum"]<-slope.t
  }
  
  #Find date -5 from the stablility point
  linear_begin<-min(derivative[!is.na(derivative$cumsum) & derivative$cumsum==running_avg,"date"])-running_avg
  
  return(linear_begin)
}

READ IN DATA

# Q: do we want to archive previous versions? Maybe an auto git mv?

##------------------------------------------
## Download and read in latest data from github
##------------------------------------------
download.file(Corona_Cases.source_url, destfile = Corona_Cases.fn)
Corona_Totals.raw <- read.csv(Corona_Cases.fn, header = T, stringsAsFactors = F)

download.file(Corona_Cases.US.source_url, destfile = Corona_Cases.US.fn)
Corona_Totals.US.raw <- read.csv(Corona_Cases.US.fn, header = T, stringsAsFactors = F)

download.file(Corona_Deaths.source_url, destfile = Corona_Deaths.fn)
Corona_Deaths.raw <- read.csv(Corona_Deaths.fn, header = T, stringsAsFactors = F)

download.file(Corona_Deaths.US.source_url, destfile = Corona_Deaths.US.fn)
Corona_Deaths.US.raw <- read.csv(Corona_Deaths.US.fn, header = T, stringsAsFactors = F)

# latest date on all data:
paste("US deaths:", names(Corona_Deaths.US.raw)[ncol(Corona_Deaths.US.raw)])
## [1] "US deaths: X5.26.20"
paste("US total:", names(Corona_Totals.US.raw)[ncol(Corona_Totals.US.raw)])
## [1] "US total: X5.26.20"
paste("World deaths:", names(Corona_Deaths.raw)[ncol(Corona_Deaths.raw)])
## [1] "World deaths: X5.26.20"
paste("World total:", names(Corona_Totals.raw)[ncol(Corona_Totals.raw)])
## [1] "World total: X5.26.20"

PROCESS DATA

  • Convert to long format
  • Fix date formatting/convert to numeric date
  • Log10 transform total # cases
##------------------------------------------
## Combine death and total data frames
##------------------------------------------
Corona_Totals.raw$case_type<-"total"
Corona_Totals.US.raw$case_type<-"total"
Corona_Deaths.raw$case_type<-"death"
Corona_Deaths.US.raw$case_type<-"death"

# for some reason, Population listed in US death file but not for other data... Weird. When combining, all datasets will have this column, but US deaths is the only useful one.  
Corona_Totals.US.raw$Population<-"NA" 
Corona_Totals.raw$Population<-"NA"
Corona_Deaths.raw$Population<-"NA"

Corona_Cases.raw<-rbind(Corona_Totals.raw,Corona_Deaths.raw)
Corona_Cases.US.raw<-rbind(Corona_Totals.US.raw,Corona_Deaths.US.raw)
#TODO: custom utils- setdiff, intersect names... option to output in merging too
##------------------------------------------
# prepare raw datasets for eventual combining
##------------------------------------------
Corona_Cases.raw$City<-"NA" # US-level data has Cities
Corona_Cases.US.raw$Country_Region<-"US_state" # To differentiate from World-level stats

Corona_Cases.US.raw<-plyr::rename(Corona_Cases.US.raw,c("Province_State"="Province.State",
                                                  "Country_Region"="Country.Region",
                                                  "Long_"="Long",
                                                  "Admin2"="City"))


##------------------------------------------
## Convert to long format
##------------------------------------------
#JHU has a gross file format. It's in wide format with each column is the date in MM/DD/YY. So read this in as raw data but trasnform it to be better suited for analysis
# Furthermore, the World and US level data is formatted differently, containing different columns, etc. Recitfy this and combine the world-level stats with U.S. level stats.

Corona_Cases.long<-rbind(make_long(select(Corona_Cases.US.raw,-c(UID,iso2,iso3,code3,FIPS,Combined_Key))),
make_long(Corona_Cases.raw))


##------------------------------------------
## Fix date formatting, convert to numeric date
##------------------------------------------
Corona_Cases.long$Date<-gsub(Corona_Cases.long$Date,pattern = "^X",replacement = "0") # leading 0 read in as X
Corona_Cases.long$Date<-gsub(Corona_Cases.long$Date,pattern = "20$",replacement = "2020") # ends in .20 and not 2020
Corona_Cases.long$Date<-as.Date(Corona_Cases.long$Date,format = "%m.%d.%y")
Corona_Cases.long$Date.numeric<-as.numeric(Corona_Cases.long$Date)

kable(table(select(Corona_Cases.long,c("Country.Region","case_type"))),caption = "Number of death and total case longitudinal datapoints per geographical region")
Number of death and total case longitudinal datapoints per geographical region
death total
Afghanistan 126 126
Albania 126 126
Algeria 126 126
Andorra 126 126
Angola 126 126
Antigua and Barbuda 126 126
Argentina 126 126
Armenia 126 126
Australia 1008 1008
Austria 126 126
Azerbaijan 126 126
Bahamas 126 126
Bahrain 126 126
Bangladesh 126 126
Barbados 126 126
Belarus 126 126
Belgium 126 126
Belize 126 126
Benin 126 126
Bhutan 126 126
Bolivia 126 126
Bosnia and Herzegovina 126 126
Botswana 126 126
Brazil 126 126
Brunei 126 126
Bulgaria 126 126
Burkina Faso 126 126
Burma 126 126
Burundi 126 126
Cabo Verde 126 126
Cambodia 126 126
Cameroon 126 126
Canada 1764 1764
Central African Republic 126 126
Chad 126 126
Chile 126 126
China 4158 4158
Colombia 126 126
Comoros 126 126
Congo (Brazzaville) 126 126
Congo (Kinshasa) 126 126
Costa Rica 126 126
Cote d’Ivoire 126 126
Croatia 126 126
Cuba 126 126
Cyprus 126 126
Czechia 126 126
Denmark 378 378
Diamond Princess 126 126
Djibouti 126 126
Dominica 126 126
Dominican Republic 126 126
Ecuador 126 126
Egypt 126 126
El Salvador 126 126
Equatorial Guinea 126 126
Eritrea 126 126
Estonia 126 126
Eswatini 126 126
Ethiopia 126 126
Fiji 126 126
Finland 126 126
France 1386 1386
Gabon 126 126
Gambia 126 126
Georgia 126 126
Germany 126 126
Ghana 126 126
Greece 126 126
Grenada 126 126
Guatemala 126 126
Guinea 126 126
Guinea-Bissau 126 126
Guyana 126 126
Haiti 126 126
Holy See 126 126
Honduras 126 126
Hungary 126 126
Iceland 126 126
India 126 126
Indonesia 126 126
Iran 126 126
Iraq 126 126
Ireland 126 126
Israel 126 126
Italy 126 126
Jamaica 126 126
Japan 126 126
Jordan 126 126
Kazakhstan 126 126
Kenya 126 126
Korea, South 126 126
Kosovo 126 126
Kuwait 126 126
Kyrgyzstan 126 126
Laos 126 126
Latvia 126 126
Lebanon 126 126
Lesotho 126 126
Liberia 126 126
Libya 126 126
Liechtenstein 126 126
Lithuania 126 126
Luxembourg 126 126
Madagascar 126 126
Malawi 126 126
Malaysia 126 126
Maldives 126 126
Mali 126 126
Malta 126 126
Mauritania 126 126
Mauritius 126 126
Mexico 126 126
Moldova 126 126
Monaco 126 126
Mongolia 126 126
Montenegro 126 126
Morocco 126 126
Mozambique 126 126
MS Zaandam 126 126
Namibia 126 126
Nepal 126 126
Netherlands 630 630
New Zealand 126 126
Nicaragua 126 126
Niger 126 126
Nigeria 126 126
North Macedonia 126 126
Norway 126 126
Oman 126 126
Pakistan 126 126
Panama 126 126
Papua New Guinea 126 126
Paraguay 126 126
Peru 126 126
Philippines 126 126
Poland 126 126
Portugal 126 126
Qatar 126 126
Romania 126 126
Russia 126 126
Rwanda 126 126
Saint Kitts and Nevis 126 126
Saint Lucia 126 126
Saint Vincent and the Grenadines 126 126
San Marino 126 126
Sao Tome and Principe 126 126
Saudi Arabia 126 126
Senegal 126 126
Serbia 126 126
Seychelles 126 126
Sierra Leone 126 126
Singapore 126 126
Slovakia 126 126
Slovenia 126 126
Somalia 126 126
South Africa 126 126
South Sudan 126 126
Spain 126 126
Sri Lanka 126 126
Sudan 126 126
Suriname 126 126
Sweden 126 126
Switzerland 126 126
Syria 126 126
Taiwan* 126 126
Tajikistan 126 126
Tanzania 126 126
Thailand 126 126
Timor-Leste 126 126
Togo 126 126
Trinidad and Tobago 126 126
Tunisia 126 126
Turkey 126 126
Uganda 126 126
Ukraine 126 126
United Arab Emirates 126 126
United Kingdom 1386 1386
Uruguay 126 126
US 126 126
US_state 410886 410886
Uzbekistan 126 126
Venezuela 126 126
Vietnam 126 126
West Bank and Gaza 126 126
Western Sahara 126 126
Yemen 126 126
Zambia 126 126
Zimbabwe 126 126 
# Decouple population and lat/long data, refactor to make it more tidy
metadata_columns<-c("Lat","Long","Population")
metadata<-unique(select(filter(Corona_Cases.long,case_type=="death"),c("Country.Region","Province.State","City",all_of(metadata_columns))))
Corona_Cases.long<-select(Corona_Cases.long,-all_of(metadata_columns))

# Some counties are not summarized on the country level. collapse all but US
Corona_Cases.long<-rbind.fill(ddply(filter(Corona_Cases.long,!Country.Region=="US_state"),c("case_type","Country.Region","Date","Date.numeric"),summarise,Total_confirmed_cases=sum(Total_confirmed_cases)),filter(Corona_Cases.long,Country.Region=="US_state"))

# Put total case and deaths side-by-side (wide)
Corona_Cases<-spread(Corona_Cases.long,key = case_type,value = Total_confirmed_cases)

#Compute moratlity rate
Corona_Cases$mortality_rate<-Corona_Cases$death/Corona_Cases$total

#TMP
Corona_Cases<-plyr::rename(Corona_Cases,c("total"="Total_confirmed_cases","death"="Total_confirmed_deaths"))

##------------------------------------------
## log10 transform total # cases
##------------------------------------------
Corona_Cases$Total_confirmed_cases.log<-log(Corona_Cases$Total_confirmed_cases,10)
Corona_Cases$Total_confirmed_deaths.log<-log(Corona_Cases$Total_confirmed_deaths,10)
##------------------------------------------
       
##------------------------------------------
## Compute # of days since 100th for US data
##------------------------------------------

# Find day that 100th case was found for Country/Province. NOTE: Non US countries may have weird provinces. For example, Frane is summairzed at the country level but also had 3 providences. I've only ensured the U.S. case100 works... so the case100_date for U.S. is summarized both for the entire country (regardless of state) and on a per-state level. 
# TODO: consider city-level summary as well. This data may be sparse

Corona_Cases<-merge(Corona_Cases,ddply(filter(Corona_Cases,Total_confirmed_cases>100),c("Country.Region"),summarise,case100_date=min(Date.numeric)))
Corona_Cases$Days_since_100<-Corona_Cases$Date.numeric-Corona_Cases$case100_date

##------------------------------------------
## Add population and lat/long data (CURRENTLY US ONLY)
##------------------------------------------

kable(filter(metadata,(is.na(Country.Region) | is.na(Population) )) %>% select(c("Country.Region","Province.State","City")) %>% unique(),caption = "Regions for which either population or Country is NA")
Regions for which either population or Country is NA
Country.Region Province.State City 
# Drop missing data 
metadata<-filter(metadata,!(is.na(Country.Region) | is.na(Population) ))
# Convert remaining pop to numeric
metadata$Population<-as.numeric(metadata$Population)
## Warning: NAs introduced by coercion
# Add metadata to cases
Corona_Cases<-merge(Corona_Cases,metadata,all.x = T)

##------------------------------------------
## Compute total and death cases relative to population 
##------------------------------------------

Corona_Cases$Total_confirmed_cases.per100<-100*Corona_Cases$Total_confirmed_cases/Corona_Cases$Population
Corona_Cases$Total_confirmed_deaths.per100<-100*Corona_Cases$Total_confirmed_deaths/Corona_Cases$Population


##------------------------------------------
## Filter df for US state-wide stats
##------------------------------------------

Corona_Cases.US_state<-filter(Corona_Cases,Country.Region=="US_state" & Total_confirmed_cases>0 ) 
kable(table(select(Corona_Cases.US_state,c("Province.State"))),caption = "Number of longitudinal datapoints (total/death) per state")
Number of longitudinal datapoints (total/death) per state
Var1 Freq
Alabama 4266
Alaska 792
Arizona 1076
Arkansas 4455
California 4104
Colorado 3887
Connecticut 637
Delaware 263
Diamond Princess 71
District of Columbia 72
Florida 4566
Georgia 10171
Grand Princess 72
Guam 72
Hawaii 372
Idaho 2024
Illinois 5808
Indiana 5917
Iowa 5382
Kansas 4440
Kentucky 6473
Louisiana 4280
Maine 1084
Maryland 1673
Massachusetts 1085
Michigan 5164
Minnesota 4843
Mississippi 5305
Missouri 5841
Montana 1820
Nebraska 3327
Nevada 807
New Hampshire 752
New Jersey 1619
New Mexico 1780
New York 4091
North Carolina 6236
North Dakota 2092
Northern Mariana Islands 57
Ohio 5565
Oklahoma 4195
Oregon 2141
Pennsylvania 4400
Puerto Rico 72
Rhode Island 424
South Carolina 3099
South Dakota 2660
Tennessee 6010
Texas 12546
Utah 993
Vermont 1005
Virgin Islands 72
Virginia 7852
Washington 2800
West Virginia 2853
Wisconsin 4202
Wyoming 1297 
Corona_Cases.US_state<-merge(Corona_Cases.US_state,ddply(filter(Corona_Cases.US_state,Total_confirmed_cases>100),c("Province.State"),summarise,case100_date_state=min(Date.numeric)))
Corona_Cases.US_state$Days_since_100_state<-Corona_Cases.US_state$Date.numeric-Corona_Cases.US_state$case100_date_state

ANALYSIS

Q1: What is the trend in cases, mortality across geopgraphical regions?

Plot # of cases vs time
* For each geographical set:
* comparative longitudinal case trend (absolute & log scale)
* comparative longitudinal mortality trend
* death vs total correlation

question dataset x y color facet pch dimentions
comparative_longitudinal_case_trend long time log_cases geography none (case type?) case_type [15, 50, 4] geography x (2 scale?) case type
comparative longitudinal case trend long time cases geography case_type ? [15, 50, 4] geography x (2+ scale) case type
comparative longitudinal mortality trend wide time mortality rate geography none none [15, 50, 4] geography
death vs total correlation wide cases deaths geography none none [15, 50, 4] geography 
# total cases vs time
# death cases vs time
# mortality rate vs time
# death vs mortality


  # death vs mortality
  # total & death case vs time (same plot)

#<question> <x> <y> <colored> <facet> <dataset>
## trend in case/deaths over time, comapred across regions <time> <log cases> <geography*> <none> <.wide>
## trend in case/deaths over time, comapred across regions <time> <cases> <geography*> <case_type> <.long>
## trend in mortality rate over time, comapred across regions <time> <mortality rate> <geography*> <none>
## how are death/mortality related/correlated? <time> <log cases> <geography*> <none>
## how are death and case load correlated? <cases> <deaths>

# lm for each?? - > apply lm from each region starting from 100th case. m, b associated with each.
    # input: geographical regsion, logcase vs day (100th case)
    # output: m, b for each geographical region ID



#total/death on same plot-  diffeer by 2 logs, so when plotting log, use pch. when plotting absolute, need to use free scales
#when plotting death and case on same, melt. 

#CoronaCases - > filter sets (3)
  #world - choose countries with sufficent data

N<-ddply(filter(Corona_Cases,Total_confirmed_cases>100),c("Country.Region"),summarise,n=length(Country.Region))
ggplot(filter(N,n<100),aes(x=n))+
  geom_histogram()+
  default_theme+
  ggtitle("Distribution of number of days with at least 100 confirmed cases for each region")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

kable(arrange(N,-n),caption="Sorted number of days with at least 100 confirmed cases")
Sorted number of days with at least 100 confirmed cases
Country.Region n
US_state 38588
China 126
Diamond Princess 107
Korea, South 97
Japan 96
Italy 94
Iran 91
Singapore 88
France 87
Germany 87
Spain 86
US 85
Switzerland 83
United Kingdom 83
Belgium 82
Netherlands 82
Norway 82
Sweden 82
Austria 80
Malaysia 79
Australia 78
Bahrain 78
Denmark 78
Canada 77
Qatar 77
Iceland 76
Brazil 75
Czechia 75
Finland 75
Greece 75
Iraq 75
Israel 75
Portugal 75
Slovenia 75
Egypt 74
Estonia 74
India 74
Ireland 74
Kuwait 74
Philippines 74
Poland 74
Romania 74
Saudi Arabia 74
Indonesia 73
Lebanon 73
San Marino 73
Thailand 73
Chile 72
Pakistan 72
Luxembourg 71
Peru 71
Russia 71
Ecuador 70
Mexico 70
Slovakia 70
South Africa 70
United Arab Emirates 70
Armenia 69
Colombia 69
Croatia 69
Panama 69
Serbia 69
Taiwan* 69
Turkey 69
Argentina 68
Bulgaria 68
Latvia 68
Uruguay 68
Algeria 67
Costa Rica 67
Dominican Republic 67
Hungary 67
Andorra 66
Bosnia and Herzegovina 66
Jordan 66
Lithuania 66
Morocco 66
New Zealand 66
North Macedonia 66
Vietnam 66
Albania 65
Cyprus 65
Malta 65
Moldova 65
Brunei 64
Burkina Faso 64
Sri Lanka 64
Tunisia 64
Ukraine 63
Azerbaijan 62
Ghana 62
Kazakhstan 62
Oman 62
Senegal 62
Venezuela 62
Afghanistan 61
Cote d’Ivoire 61
Cuba 60
Mauritius 60
Uzbekistan 60
Cambodia 59
Cameroon 59
Honduras 59
Nigeria 59
West Bank and Gaza 59
Belarus 58
Georgia 58
Bolivia 57
Kosovo 57
Kyrgyzstan 57
Montenegro 57
Congo (Kinshasa) 56
Kenya 55
Niger 54
Guinea 53
Rwanda 53
Trinidad and Tobago 53
Paraguay 52
Bangladesh 51
Djibouti 49
El Salvador 48
Guatemala 47
Madagascar 46
Mali 45
Congo (Brazzaville) 42
Jamaica 42
Gabon 40
Somalia 40
Tanzania 40
Ethiopia 39
Burma 38
Sudan 37
Liberia 36
Maldives 34
Equatorial Guinea 33
Cabo Verde 31
Sierra Leone 29
Guinea-Bissau 28
Togo 28
Zambia 27
Eswatini 26
Chad 25
Tajikistan 24
Haiti 22
Sao Tome and Principe 22
Benin 20
Nepal 20
Uganda 20
Central African Republic 19
South Sudan 19
Guyana 17
Mozambique 16
Yemen 12
Mongolia 11
Mauritania 8
Nicaragua 8
Malawi 2
Syria 2 
# Pick top 15 countries with data
max_colors<-12
# find way to fix this- China has diff provences. Plot doesnt look right...
sufficient_data<-arrange(filter(N,!Country.Region %in% c("US_state", "Diamond Princess")),-n)[1:max_colors,]
kable(sufficient_data,caption = paste0("Top ",max_colors," countries with sufficient data"))
Top 12 countries with sufficient data
Country.Region n
China 126
Korea, South 97
Japan 96
Italy 94
Iran 91
Singapore 88
France 87
Germany 87
Spain 86
US 85
Switzerland 83
United Kingdom 83 
Corona_Cases.world<-filter(Corona_Cases,Country.Region %in% c(sufficient_data$Country.Region))


  #us 
  #    - by state
Corona_Cases.US<-filter(Corona_Cases,Country.Region=="US" & Total_confirmed_cases>0)
# summarize 
#!City %in% c("Unassigned") 
  #    - specific cities
#mortality_rate!=Inf & mortality_rate<=1
Corona_Cases.UScity<-filter(Corona_Cases,Province.State %in% c("Pennsylvania","Maryland","New York","New Jersey") & City %in% c("Bucks","Baltimore City", "New York","Burlington","Cape May"))

measure_vars_long<-c("Total_confirmed_cases.log","Total_confirmed_cases","Total_confirmed_deaths","Total_confirmed_deaths.log")
melt_arg_list<-list(variable.name = "case_type",value.name = "cases",measure.vars = c("Total_confirmed_cases","Total_confirmed_deaths"))
melt_arg_list$data=NULL


melt_arg_list$data=select(Corona_Cases.world,-ends_with(match = "log"))
Corona_Cases.world.long<-do.call(melt,melt_arg_list)
melt_arg_list$data=select(Corona_Cases.UScity,-ends_with(match = "log"))
Corona_Cases.UScity.long<-do.call(melt,melt_arg_list)
melt_arg_list$data=select(Corona_Cases.US_state,-ends_with(match = "log"))
Corona_Cases.US_state.long<-do.call(melt,melt_arg_list)

Corona_Cases.world.long$cases.log<-log(Corona_Cases.world.long$cases,10)
Corona_Cases.US_state.long$cases.log<-log(Corona_Cases.US_state.long$cases,10)
Corona_Cases.UScity.long$cases.log<-log(Corona_Cases.UScity.long$cases,10)


# what is the current death and total case load for US? For world? For states?
#-absolute
#-log

# what is mortality rate (US, world)
#-absolute

#how is death and case correlated? (US, world)
#-absolute
#Corona_Cases.US<-filter(Corona_Cases,Country.Region=="US" & Total_confirmed_cases>0)
#Corona_Cases.US.case100<-filter(Corona_Cases.US, Days_since_100>=0)
# linear model parameters
#(model_fit<-lm(formula = Total_confirmed_cases.log~Days_since_100,data= Corona_Cases.US.case100 ))

#(slope<-model_fit$coefficients[2])
#(intercept<-model_fit$coefficients[1])

# Correlation coefficient
#cor(x = Corona_Cases.US.case100$Days_since_100,y = Corona_Cases.US.case100$Total_confirmed_cases.log)

##------------------------------------------
## Plot World Data
##------------------------------------------
# Timestamp for world
timestamp_plot.world<-paste("Most recent date for which data available:",max(Corona_Cases.world$Date))#timestamp(quiet = T,prefix = "Updated ",suffix = " (EST)")


# Base template for plots
baseplot.world<-ggplot(data=NULL,aes(x=Days_since_100,col=Country.Region))+
  default_theme+
  scale_color_brewer(type = "qualitative",palette = "Paired")+
  ggtitle(paste("Log10 cases over time,",timestamp_plot.world))+
  theme(legend.position = "bottom",plot.title = element_text(size=12))


##/////////////////////////
### Plot Longitudinal cases

(Corona_Cases.world.long.plot<-baseplot.world+
    geom_point(data=Corona_Cases.world.long,aes(y=cases))+
    geom_line(data=Corona_Cases.world.long,aes(y=cases))+
    facet_wrap(~case_type,scales = "free_y",ncol=1)+
    ggtitle(timestamp_plot.world)
    )

(Corona_Cases.world.loglong.plot<-baseplot.world+
    geom_point(data=Corona_Cases.world.long,aes(y=cases.log))+
    geom_line(data=Corona_Cases.world.long,aes(y=cases.log))+
    facet_wrap(~case_type,scales = "free_y",ncol=1)+
    ggtitle(timestamp_plot.world))

##/////////////////////////
### Plot Longitudinal mortality rate

(Corona_Cases.world.mortality.plot<-baseplot.world+
    geom_point(data=Corona_Cases.world,aes(y=mortality_rate))+
    geom_line(data=Corona_Cases.world,aes(y=mortality_rate))+
    ylim(c(0,0.3))+
    ggtitle(timestamp_plot.world))
## Warning: Removed 100 rows containing missing values (geom_point).
## Warning: Removed 100 row(s) containing missing values (geom_path).

##/////////////////////////
### Plot death vs total case correlation

(Corona_Cases.world.casecor.plot<-ggplot(Corona_Cases.world,aes(x=Total_confirmed_cases,y=Total_confirmed_deaths,col=Country.Region))+
  geom_point()+
  geom_line()+
  default_theme+
  scale_color_brewer(type = "qualitative",palette = "Paired")+
  ggtitle(paste("Log10 cases over time,",timestamp_plot.world))+
  theme(legend.position = "bottom",plot.title = element_text(size=12))+
    ggtitle(timestamp_plot.world))

### Write polots

write_plot(Corona_Cases.world.long.plot,wd = results_dir)
## [1] "/Users/stevensmith/Projects/coronavirus/results/Corona_Cases.world.long.plot.png"
write_plot(Corona_Cases.world.loglong.plot,wd = results_dir)
## [1] "/Users/stevensmith/Projects/coronavirus/results/Corona_Cases.world.loglong.plot.png"
write_plot(Corona_Cases.world.mortality.plot,wd = results_dir)
## Warning: Removed 100 rows containing missing values (geom_point).

## Warning: Removed 100 row(s) containing missing values (geom_path).
## [1] "/Users/stevensmith/Projects/coronavirus/results/Corona_Cases.world.mortality.plot.png"
write_plot(Corona_Cases.world.casecor.plot,wd = results_dir)
## [1] "/Users/stevensmith/Projects/coronavirus/results/Corona_Cases.world.casecor.plot.png"
##------------------------------------------
## Plot US State Data
##-----------------------------------------

baseplot.US<-ggplot(data=NULL,aes(x=Days_since_100_state,col=case_type))+
  default_theme+
  facet_wrap(~Province.State)+
  ggtitle(paste("Log10 cases over time,",timestamp_plot.world))

Corona_Cases.US_state.long.plot<-baseplot.US+geom_point(data=Corona_Cases.US_state.long,aes(y=cases.log))
##------------------------------------------
## Plot US City Data
##-----------------------------------------

Corona_Cases.US.plotdata<-filter(Corona_Cases.US_state,Province.State %in% c("Pennsylvania","Maryland","New York","New Jersey") &
                                   City %in% c("Bucks","Baltimore City", "New York","Burlington","Cape May") &
                                   Total_confirmed_cases>0) 
timestamp_plot<-paste("Most recent date for which data available:",max(Corona_Cases.US.plotdata$Date))#timestamp(quiet = T,prefix = "Updated ",suffix = " (EST)")

city_colors<-c("Bucks"='#beaed4',"Baltimore City"='#386cb0', "New York"='#7fc97f',"Burlington"='#fdc086',"Cape May"="#e78ac3")

##/////////////////////////
### Plot death vs total case correlation

(Corona_Cases.city.loglong.plot<-ggplot(melt(Corona_Cases.US.plotdata,measure.vars = c("Total_confirmed_cases.log","Total_confirmed_deaths.log"),variable.name = "case_type",value.name = "cases"),aes(x=Date,y=cases,col=City,pch=case_type))+
  geom_point(size=4)+
    geom_line()+
  default_theme+
  #facet_wrap(~case_type)+
    ggtitle(paste("Log10 total and death cases over time,",timestamp_plot))+
theme(legend.position = "bottom",plot.title = element_text(size=12),axis.text.x = element_text(angle=45,hjust=1))+
    scale_color_manual(values = city_colors)+
  scale_x_date(date_breaks="1 week",date_minor_breaks="1 day"))

(Corona_Cases.city.long.plot<-ggplot(filter(Corona_Cases.US.plotdata,Province.State !="New York"),aes(x=Date,y=Total_confirmed_cases,col=City))+
  geom_point(size=4)+
  geom_line()+
  default_theme+
  facet_grid(~Province.State,scales = "free_y")+
  ggtitle(paste("MD, PA, NJ total cases over time,",timestamp_plot))+
  theme(legend.position = "bottom",plot.title = element_text(size=12),axis.text.x = element_text(angle=45,hjust=1))
+
  scale_color_manual(values = city_colors)+
  scale_x_date(date_breaks="1 week",date_minor_breaks="1 day"))

(Corona_Cases.city.mortality.plot<-ggplot(Corona_Cases.US.plotdata,aes(x=Date,y=mortality_rate,col=City))+
  geom_point(size=3)+
  geom_line(size=2)+
  default_theme+
  ggtitle(paste("Mortality rate (deaths/total) over time,",timestamp_plot))+
  theme(legend.position = "bottom",plot.title = element_text(size=12),axis.text.x = element_text(angle=45,hjust=1))+
  scale_color_manual(values = city_colors)+
  scale_x_date(date_breaks="1 week",date_minor_breaks="1 day"))

(Corona_Cases.city.casecor.plot<-ggplot(filter(Corona_Cases.US.plotdata,Province.State !="New York"),aes(y=Total_confirmed_deaths,x=Total_confirmed_cases,col=City))+
  geom_point(size=3)+
  geom_line(size=2)+
  default_theme+
  ggtitle(paste("Correlation of death vs total cases,",timestamp_plot))+
  theme(legend.position = "bottom",plot.title = element_text(size=12))+
  scale_color_manual(values = city_colors))

(Corona_Cases.city.long.normalized.plot<-ggplot(filter(Corona_Cases.US.plotdata,Province.State !="New York"),aes(x=Date,y=Total_confirmed_cases.per100,col=City))+
  geom_point(size=4)+
  geom_line()+
  default_theme+
  facet_grid(~Province.State)+
  ggtitle(paste("MD, PA, NJ total cases over time per 100 people,",timestamp_plot))+
  theme(legend.position = "bottom",plot.title = element_text(size=12),axis.text.x = element_text(angle=45,hjust=1))+
  scale_color_manual(values = city_colors)  +
  scale_x_date(date_breaks="1 week",date_minor_breaks="1 day"))

write_plot(Corona_Cases.city.long.plot,wd = results_dir_custom)
## [1] "/Users/stevensmith/Projects/coronavirus/results/custom/Corona_Cases.city.long.plot.png"
write_plot(Corona_Cases.city.loglong.plot,wd = results_dir_custom)
## [1] "/Users/stevensmith/Projects/coronavirus/results/custom/Corona_Cases.city.loglong.plot.png"
write_plot(Corona_Cases.city.mortality.plot,wd = results_dir_custom)
## [1] "/Users/stevensmith/Projects/coronavirus/results/custom/Corona_Cases.city.mortality.plot.png"
write_plot(Corona_Cases.city.casecor.plot,wd = results_dir_custom)
## [1] "/Users/stevensmith/Projects/coronavirus/results/custom/Corona_Cases.city.casecor.plot.png"
write_plot(Corona_Cases.city.long.normalized.plot,wd = results_dir_custom)
## [1] "/Users/stevensmith/Projects/coronavirus/results/custom/Corona_Cases.city.long.normalized.plot.png"

Q1b what is the model

Fit the cases to a linear model 1. Find time at which the case vs date becomes linear in each plot
2. Fit linear model for each city

# What is the predict # of cases for the next few days?
# How is the model performing historically?

Corona_Cases.US_state.summary<-ddply(Corona_Cases.US_state,
                                     c("Province.State","Date"),
                                     summarise,
                                     Total_confirmed_cases_perstate=sum(Total_confirmed_cases)) %>% 
    filter(Total_confirmed_cases_perstate>100)

# Compute the states with the most cases (for coloring and for linear model)
top_states_totals<-head(ddply(Corona_Cases.US_state.summary,c("Province.State"),summarise, Total_confirmed_cases_perstate.max=max(Total_confirmed_cases_perstate)) %>% arrange(-Total_confirmed_cases_perstate.max),n=max_colors)

kable(top_states_totals,caption = "Top 12 States, total count ")
top_states<-top_states_totals$Province.State

# Manually fix states so that Maryland is switched out for New York
top_states_modified<-c(top_states[top_states !="New York"],"Maryland")

# Plot with all states:
(Corona_Cases.US_state.summary.plot<-ggplot(Corona_Cases.US_state.summary,aes(x=Date,y=Total_confirmed_cases_perstate))+
  geom_point()+
  geom_point(data=filter(Corona_Cases.US_state.summary,Province.State %in% top_states),aes(col=Province.State))+
  scale_color_brewer(type = "qualitative",palette = "Paired")+
  default_theme+
  theme(axis.text.x = element_text(angle=45,hjust=1),legend.position = "bottom")+
  ggtitle("Total confirmed cases per state, top 12 colored")+
  scale_x_date(date_breaks="1 week",date_minor_breaks="1 day"))

##------------------------------------------
## Fit linear model to time vs total cases
##-----------------------------------------

# First, find the date at which each state's cases vs time becomes lienar (2nd derivative is about 0)
li<-ddply(Corona_Cases.US_state.summary,c("Province.State"),find_linear_index)

# Compute linear model for each state starting at the point at which data becomes linear
for(i in 1:nrow(li)){
  Province.State.i<-li[i,"Province.State"]
  date.i<-li[i,"V1"]
  data.i<-filter(Corona_Cases.US_state.summary,Province.State==Province.State.i & as.numeric(Date) >= date.i)
  model_results<-lm(data.i,formula = Total_confirmed_cases_perstate~Date)
  slope<-model_results$coefficients[2]
  intercept<-model_results$coefficients[1]
  li[li$Province.State==Province.State.i,"m"]<-slope
  li[li$Province.State==Province.State.i,"b"]<-intercept
  }

# Compute top state case load with fitted model

(Corona_Cases.US_state.lm.plot<-ggplot(filter(Corona_Cases.US_state.summary,Province.State %in% top_states_modified ))+
    geom_abline(data=filter(li,Province.State %in% top_states_modified),
                aes(slope = m,intercept = b,col=Province.State),lty=2)+
    geom_point(aes(x=Date,y=Total_confirmed_cases_perstate,col=Province.State))+
    scale_color_brewer(type = "qualitative",palette = "Paired")+
    default_theme+
    theme(axis.text.x = element_text(angle=45,hjust=1),legend.position = "bottom")+
    ggtitle("Total confirmed cases per state, top 12 colored")+
    scale_x_date(date_breaks="1 week",date_minor_breaks="1 day"))

##------------------------------------------
## Predict the number of total cases over the next week
##-----------------------------------------

predicted_days<-c(0,1,2,3,7)+as.numeric(as.Date("2020-04-20"))

predicted_days_df<-data.frame(matrix(ncol=3))
names(predicted_days_df)<-c("Province.State","days","Total_confirmed_cases_perstate")

# USe model parameters to estiamte case loads
for(state.i in top_states_modified){
  predicted_days_df<-rbind(predicted_days_df,
                           data.frame(Province.State=state.i,
                                      prediction_model(m = li[li$Province.State==state.i,"m"],
                                                       b =li[li$Province.State==state.i,"b"] ,
                                                       days =predicted_days )))
  }

predicted_days_df$Date<-as.Date(predicted_days_df$days,origin="1970-01-01")

kable(predicted_days_df,caption = "Predicted total cases over the next week for selected states")

##------------------------------------------
## Write plots
##-----------------------------------------

write_plot(Corona_Cases.US_state.summary.plot,wd = results_dir)
write_plot(Corona_Cases.US_state.lm.plot,wd = results_dir)

##------------------------------------------
## Write tables
##-----------------------------------------

write.csv(predicted_days_df,file = paste0(results_dir,"predicted_total_cases_days.csv"),quote = F,row.names = F)

Q2: What is the predicted number of cases?

What is the prediction of COVID-19 based on model thus far? Additional questions:

WHy did it take to day 40 to start a log linear trend? How long will it be till x number of cases? When will the plateu happen? Are any effects noticed with social distancing? Delays

##------------------------------------------
## Prediction and Prediction Accuracy
##------------------------------------------


today_num<-max(Corona_Cases.US$Days_since_100)
predicted_days<-today_num+c(1,2,3,7)

#mods = dlply(mydf, .(x3), lm, formula = y ~ x1 + x2)
#today:
Corona_Cases.US[Corona_Cases.US$Days_since_100==(today_num-1),]
Corona_Cases.US[Corona_Cases.US$Days_since_100==today_num,]
Corona_Cases.US$type<-"Historical"


#prediction_values<-prediction_model(m=slope,b=intercept,days = predicted_days)$Total_confirmed_cases

histoical_model<-data.frame(date=today_num,m=slope,b=intercept)
tmp<-data.frame(state=rep(c("A","B"),each=3),x=c(1,2,3,4,5,6))
tmp$y<-c(tmp[1:3,"x"]+5,tmp[4:6,"x"]*5+1)
ddply(tmp,c("state"))
lm(data =tmp,formula = y~x )

train_lm<-function(input_data,subset_coulmn,formula_input){
case_models <- dlply(input_data, subset_coulmn, lm, formula = formula_input)
case_models.parameters <- ldply(case_models, coef)
case_models.parameters<-rename(case_models.parameters,c("b"="(Intercept)","m"=subset_coulmn))
return(case_models.parameters)
}

train_lm(tmp,"state")

 dlply(input_data, subset_coulmn, lm,m=)
 
# model for previous y days
#historical_model_predictions<-data.frame(day_x=NULL,Days_since_100=NULL,Total_confirmed_cases=NULL,Total_confirmed_cases.log=NULL)
# for(i in c(1,2,3,4,5,6,7,8,9,10)){
#   #i<-1
# day_x<-today_num-i # 1, 2, 3, 4
# day_x_nextweek<-day_x+c(1,2,3)
# model_fit_x<-lm(data = filter(Corona_Cases.US.case100,Days_since_100 < day_x),formula = Total_confirmed_cases.log~Days_since_100)
# prediction_day_x_nextweek<-prediction_model(m = model_fit_x$coefficients[2],b = model_fit_x$coefficients[1],days = day_x_nextweek)
# prediction_day_x_nextweek$type<-"Predicted"
# acutal_day_x_nextweek<-filter(Corona_Cases.US,Days_since_100 %in% day_x_nextweek) %>% select(c(Days_since_100,Total_confirmed_cases,Total_confirmed_cases.log))
# acutal_day_x_nextweek$type<-"Historical"
# historical_model_predictions.i<-data.frame(day_x=day_x,rbind(acutal_day_x_nextweek,prediction_day_x_nextweek))
# historical_model_predictions<-rbind(historical_model_predictions.i,historical_model_predictions)
# }

#historical_model_predictions.withHx<-rbind.fill(historical_model_predictions,data.frame(Corona_Cases.US,type="Historical"))
#historical_model_predictions.withHx$Total_confirmed_cases.log2<-log(historical_model_predictions.withHx$Total_confirmed_cases,2)

(historical_model_predictions.plot<-ggplot(historical_model_predictions.withHx,aes(x=Days_since_100,y=Total_confirmed_cases.log,col=type))+
    geom_point(size=3)+
    default_theme+
    theme(legend.position = "bottom")+ 
      #geom_abline(slope = slope,intercept =intercept,lty=2)+
    #facet_wrap(~case_type,ncol=1)+
    scale_color_manual(values = c("Historical"="#377eb8","Predicted"="#e41a1c")))
write_plot(historical_model_predictions.plot,wd=results_dir)

Q3: What is the effect on social distancing, descreased mobility on case load?

Load data from Google which compoutes % change in user mobility relative to baseline for * Recreation
* Workplace
* Residence
* Park
* Grocery

Data from https://www.google.com/covid19/mobility/

# See pre-processing section for script on gathering mobility data

# UNDER DEVELOPMENT

mobility<-read.csv("/Users/stevensmith/Projects/MIT_COVID19/mobility.csv",header = T,stringsAsFactors = F)
#mobility$Retail_Recreation<-as.numeric(sub(mobility$Retail_Recreation,pattern = "%",replacement = ""))
#mobility$Workplace<-as.numeric(sub(mobility$Workplace,pattern = "%",replacement = ""))
#mobility$Residential<-as.numeric(sub(mobility$Residential,pattern = "%",replacement = ""))

##------------------------------------------
## Show relationship between mobility and caseload
##------------------------------------------
mobility$County<-gsub(mobility$County,pattern = " County",replacement = "")
Corona_Cases.US_state.mobility<-merge(Corona_Cases.US_state,plyr::rename(mobility,c("State"="Province.State","County"="City")))

#Corona_Cases.US_state.tmp<-merge(metadata,Corona_Cases.US_state.tmp)
# Needs to happen upsteam, see todos
#Corona_Cases.US_state.tmp$Total_confirmed_cases.perperson<-Corona_Cases.US_state.tmp$Total_confirmed_cases/as.numeric(Corona_Cases.US_state.tmp$Population)
mobility_measures<-c("Retail_Recreation","Grocery_Pharmacy","Parks","Transit","Workplace","Residential")

plot_data<-filter(Corona_Cases.US_state.mobility, Date.numeric==max(Corona_Cases.US_state$Date.numeric) ) %>% melt(measure.vars=mobility_measures) 
plot_data$value<-as.numeric(gsub(plot_data$value,pattern = "%",replacement = ""))
plot_data<-filter(plot_data,!is.na(value))

(mobility.plot<-ggplot(filter(plot_data,Province.State %in% c("Pennsylvania","Maryland","New Jersey","California","Delaware","Connecticut")),aes(y=Total_confirmed_cases.per100,x=value))+geom_point()+
  facet_grid(Province.State~variable,scales = "free")+
  xlab("Mobility change from baseline (%)")+
  ylab(paste0("Confirmed cases per 100 people(Today)"))+
  default_theme+
  ggtitle("Mobility change vs cases"))

(mobility.global.plot<-ggplot(plot_data,aes(y=Total_confirmed_cases.per100,x=value))+geom_point()+
  facet_wrap(~variable,scales = "free")+
  xlab("Mobility change from baseline (%)")+
  ylab(paste0("Confirmed cases (Today) per 100 people"))+
  default_theme+
  ggtitle("Mobility change vs cases"))

plot_data.permobility_summary<-ddply(plot_data,c("Province.State","variable"),summarise,cor=cor(y =Total_confirmed_cases.per100,x=value),median_change=median(x=value)) %>% arrange(-abs(cor))

kable(plot_data.permobility_summary,caption = "Ranked per-state mobility correlation with total confirmed cases")
Ranked per-state mobility correlation with total confirmed cases
Province.State variable cor median_change
Alaska Transit -1.0000000 -63.0
Delaware Retail_Recreation 1.0000000 -39.5
Delaware Grocery_Pharmacy 1.0000000 -17.5
Delaware Parks -1.0000000 20.5
Delaware Transit 1.0000000 -37.0
Delaware Workplace 1.0000000 -37.0
Delaware Residential -1.0000000 14.0
Hawaii Retail_Recreation 0.9931972 -56.0
Hawaii Grocery_Pharmacy 0.9695437 -34.0
New Hampshire Parks 0.9582784 -20.0
Connecticut Grocery_Pharmacy -0.9087695 -6.0
Maine Transit -0.9040896 -50.0
Alaska Residential 0.8872480 13.0
Utah Residential -0.8675952 12.0
South Dakota Parks 0.8656364 -26.0
Vermont Parks 0.8542006 -35.5
Alaska Grocery_Pharmacy -0.8062819 -7.0
Hawaii Residential -0.7854909 19.0
Utah Transit -0.7846772 -18.0
Massachusetts Workplace -0.7625388 -39.0
Connecticut Transit -0.7616657 -50.0
Rhode Island Workplace -0.7503039 -39.5
Wyoming Parks -0.7347997 -4.0
Alaska Workplace -0.7314780 -34.0
Wyoming Transit -0.7208980 -17.0
Utah Parks -0.6853389 17.0
Hawaii Parks 0.6813458 -72.0
Vermont Grocery_Pharmacy -0.6540895 -25.0
New York Workplace -0.6469077 -34.5
Utah Workplace -0.6448624 -37.0
Maine Workplace -0.6433751 -30.0
Arizona Grocery_Pharmacy -0.6397647 -15.0
Rhode Island Retail_Recreation -0.6273853 -45.0
Montana Workplace -0.6239388 -40.5
Hawaii Transit 0.6188732 -89.0
Rhode Island Residential -0.6164663 18.5
Nebraska Workplace 0.6064648 -32.5
New Jersey Workplace -0.6030508 -44.0
New Jersey Parks -0.5973055 -6.0
New York Retail_Recreation -0.5872062 -46.0
Connecticut Residential 0.5408283 14.0
Hawaii Workplace 0.5396454 -46.0
North Dakota Retail_Recreation -0.5366923 -42.0
New York Parks 0.5276211 20.0
Connecticut Retail_Recreation -0.5187090 -45.0
Massachusetts Retail_Recreation -0.5173605 -44.0
North Dakota Parks 0.5131966 -34.0
Arizona Retail_Recreation -0.5094635 -42.5
Maine Parks 0.5035091 -31.0
Connecticut Workplace -0.5023981 -39.0
New Jersey Retail_Recreation -0.5022912 -62.5
Montana Parks -0.4913929 -58.0
Wyoming Workplace -0.4876002 -31.0
Nebraska Residential -0.4855502 14.0
New Jersey Grocery_Pharmacy -0.4841000 2.5
New Mexico Grocery_Pharmacy -0.4729771 -11.0
Rhode Island Parks 0.4729613 52.0
Montana Residential 0.4701424 14.0
Iowa Parks -0.4668678 28.5
New Mexico Parks 0.4492647 -31.5
Illinois Transit -0.4485191 -31.0
New Mexico Residential 0.4475620 13.5
Kansas Parks 0.4429949 72.0
Pennsylvania Workplace -0.4334272 -36.0
Vermont Residential 0.4326972 11.5
South Carolina Workplace 0.4308558 -30.0
New Jersey Transit -0.4281583 -50.5
Idaho Workplace -0.4272893 -29.0
Arizona Residential 0.4242786 13.0
Kentucky Parks -0.4210859 28.5
California Transit -0.4205470 -42.0
Wisconsin Transit -0.4194766 -23.5
Massachusetts Grocery_Pharmacy -0.4187413 -7.0
New Hampshire Residential -0.4173083 14.0
Montana Retail_Recreation -0.4145442 -51.0
California Residential 0.4100448 14.0
Idaho Grocery_Pharmacy -0.4028251 -4.5
Idaho Transit -0.3997869 -30.0
Maryland Workplace -0.3966089 -35.0
Maryland Grocery_Pharmacy -0.3959632 -10.0
Alabama Grocery_Pharmacy -0.3951991 -2.0
Nevada Transit -0.3925209 -20.0
Montana Transit -0.3892824 -41.0
Arizona Transit 0.3811607 -38.0
Alabama Workplace -0.3804983 -29.0
New York Transit -0.3734401 -48.0
Wyoming Grocery_Pharmacy -0.3665922 -10.0
West Virginia Parks 0.3664071 -33.0
Pennsylvania Retail_Recreation -0.3563090 -45.0
New Mexico Retail_Recreation -0.3498975 -42.5
Michigan Parks 0.3415369 30.0
Nebraska Grocery_Pharmacy 0.3391776 -0.5
Florida Residential 0.3316478 14.0
Pennsylvania Parks 0.3292591 13.0
California Parks -0.3289171 -38.5
Montana Grocery_Pharmacy -0.3279939 -16.0
Alabama Transit -0.3259993 -36.5
Alaska Retail_Recreation 0.3245901 -39.0
North Dakota Workplace 0.3153823 -40.0
Minnesota Transit -0.3117365 -28.5
North Carolina Grocery_Pharmacy 0.3108803 0.0
Maine Retail_Recreation -0.3088429 -42.0
Arkansas Parks -0.3039396 -12.0
West Virginia Grocery_Pharmacy -0.3036763 -6.0
Vermont Retail_Recreation 0.3014240 -57.0
Idaho Retail_Recreation -0.2950909 -40.5
Colorado Residential 0.2881398 14.0
Mississippi Residential 0.2863912 13.0
Maryland Retail_Recreation -0.2832871 -39.0
Virginia Transit -0.2818214 -33.0
Arkansas Retail_Recreation -0.2789239 -30.0
Texas Residential -0.2763167 15.0
Rhode Island Transit -0.2749771 -56.0
Kansas Workplace 0.2727601 -32.5
North Carolina Workplace 0.2719939 -31.0
Vermont Workplace -0.2673684 -43.0
Nevada Residential 0.2631416 17.0
Maryland Residential 0.2626017 15.0
Utah Retail_Recreation -0.2610567 -40.0
Oregon Grocery_Pharmacy 0.2604544 -7.0
Rhode Island Grocery_Pharmacy 0.2590564 -7.5
Nevada Retail_Recreation -0.2584506 -43.0
Texas Workplace 0.2580831 -32.0
Illinois Workplace -0.2516389 -31.0
Tennessee Workplace -0.2508101 -31.0
Texas Parks 0.2503155 -42.0
Florida Parks -0.2489220 -43.0
Wisconsin Parks 0.2478975 51.5
Tennessee Residential 0.2465179 11.5
Illinois Parks 0.2464331 26.5
South Carolina Parks -0.2421267 -23.0
California Grocery_Pharmacy -0.2413883 -11.5
Pennsylvania Grocery_Pharmacy -0.2411169 -6.0
California Retail_Recreation -0.2364956 -44.0
Georgia Grocery_Pharmacy -0.2333103 -10.0
Missouri Residential -0.2315271 13.0
Arkansas Residential 0.2314189 12.0
New York Grocery_Pharmacy -0.2313466 8.0
Washington Workplace -0.2231765 -38.0
Idaho Residential -0.2139764 11.0
North Carolina Transit 0.2127722 -32.0
Michigan Workplace -0.2111759 -40.0
California Workplace -0.2094557 -36.0
New Jersey Residential 0.2076436 18.0
North Carolina Residential 0.2015529 13.0
Kansas Grocery_Pharmacy -0.1999844 -14.0
Mississippi Grocery_Pharmacy -0.1974360 -8.0
Iowa Transit 0.1944439 -24.0
Illinois Residential 0.1931903 14.0
Oregon Residential 0.1913519 10.5
Georgia Workplace -0.1892298 -33.5
North Dakota Grocery_Pharmacy -0.1883518 -8.0
Missouri Workplace 0.1883338 -28.5
Colorado Parks -0.1789064 2.0
South Dakota Transit -0.1747711 -40.0
New Mexico Transit 0.1725590 -38.5
Georgia Retail_Recreation -0.1716591 -41.0
Virginia Residential 0.1710728 14.0
Wisconsin Residential -0.1676647 14.0
Connecticut Parks 0.1654319 43.0
Florida Retail_Recreation 0.1624300 -43.0
Ohio Transit 0.1618303 -28.0
Virginia Grocery_Pharmacy -0.1589315 -8.0
Washington Residential 0.1548912 13.0
Minnesota Parks 0.1539478 -9.0
South Carolina Residential -0.1533238 12.0
Virginia Parks 0.1516374 6.0
Georgia Residential -0.1508705 13.0
Oklahoma Residential 0.1500992 15.0
Indiana Retail_Recreation 0.1472029 -38.0
New Hampshire Retail_Recreation -0.1426800 -41.0
Alabama Parks 0.1420917 -1.0
South Dakota Retail_Recreation -0.1418970 -38.5
Massachusetts Parks 0.1406265 39.0
Indiana Residential 0.1395374 12.0
Mississippi Transit -0.1378276 -38.5
Michigan Retail_Recreation -0.1372488 -53.0
North Dakota Transit 0.1366526 -48.0
Massachusetts Transit -0.1343047 -45.0
Alabama Retail_Recreation 0.1321146 -39.0
Washington Grocery_Pharmacy 0.1320256 -7.0
South Dakota Residential 0.1317904 15.0
Oregon Retail_Recreation 0.1314695 -41.0
Pennsylvania Transit -0.1300246 -41.5
Ohio Parks -0.1286516 67.5
Maine Residential -0.1273953 11.0
Washington Transit -0.1267337 -33.5
North Carolina Parks -0.1189864 7.0
Texas Transit 0.1189128 -41.0
Wyoming Retail_Recreation -0.1182525 -39.0
Oregon Parks 0.1158120 16.5
Oklahoma Parks -0.1142092 -18.5
Kansas Transit -0.1139995 -26.5
Kentucky Grocery_Pharmacy 0.1135633 4.0
Florida Workplace -0.1121572 -33.0
New Hampshire Grocery_Pharmacy -0.1121380 -6.0
Massachusetts Residential 0.1119788 15.0
Mississippi Retail_Recreation -0.1109884 -40.0
Wisconsin Workplace -0.1104923 -31.0
Minnesota Workplace -0.1097801 -33.0
Maryland Transit -0.1097332 -39.0
Texas Grocery_Pharmacy 0.1089536 -14.0
Idaho Parks 0.1083222 -22.0
Arkansas Workplace -0.1078073 -26.0
Wyoming Residential 0.1061846 12.5
Arkansas Transit 0.1053288 -27.0
Arizona Workplace -0.1047115 -35.0
Ohio Residential 0.1043167 14.0
Nebraska Retail_Recreation 0.1042186 -36.0
Minnesota Retail_Recreation 0.1026423 -40.0
Maine Grocery_Pharmacy -0.1025823 -13.0
Indiana Parks -0.1016907 29.0
South Dakota Workplace 0.1014981 -35.0
Mississippi Workplace -0.1004460 -33.0
Wisconsin Grocery_Pharmacy 0.0991707 -1.0
Georgia Parks 0.0925718 -6.0
New York Residential 0.0920025 17.5
Oklahoma Grocery_Pharmacy -0.0911595 -1.0
New Hampshire Transit -0.0903033 -57.0
Virginia Workplace -0.0902029 -31.5
Indiana Workplace 0.0896554 -34.0
Pennsylvania Residential 0.0875187 15.0
West Virginia Residential -0.0871474 11.0
Missouri Transit -0.0862899 -24.5
Kentucky Transit 0.0825242 -31.0
Virginia Retail_Recreation -0.0815464 -35.0
South Carolina Transit 0.0808945 -45.0
Nevada Parks 0.0804126 -12.5
Michigan Grocery_Pharmacy -0.0798123 -11.0
Nebraska Transit -0.0771981 -9.0
Kentucky Retail_Recreation 0.0764110 -29.0
Indiana Grocery_Pharmacy -0.0757445 -5.5
South Carolina Retail_Recreation -0.0746272 -35.0
Michigan Residential 0.0745420 15.0
Colorado Transit 0.0738397 -36.0
Tennessee Parks -0.0734331 10.5
Washington Parks 0.0713444 -3.5
Ohio Grocery_Pharmacy 0.0710696 0.0
Michigan Transit 0.0686961 -46.0
North Dakota Residential -0.0651724 17.0
Oregon Workplace -0.0646101 -31.0
Minnesota Grocery_Pharmacy 0.0642601 -6.0
Kentucky Residential 0.0624202 12.0
North Carolina Retail_Recreation 0.0620898 -34.0
Ohio Retail_Recreation 0.0605859 -36.0
West Virginia Workplace 0.0602897 -33.0
Nebraska Parks 0.0595751 55.5
Oklahoma Workplace 0.0585898 -31.0
West Virginia Retail_Recreation -0.0573394 -38.5
South Dakota Grocery_Pharmacy 0.0548553 -9.0
Washington Retail_Recreation -0.0531770 -42.0
South Carolina Grocery_Pharmacy 0.0527702 1.0
Iowa Retail_Recreation -0.0514844 -38.0
New Hampshire Workplace 0.0512132 -37.0
Oregon Transit 0.0500688 -27.5
Florida Grocery_Pharmacy 0.0486905 -14.0
Missouri Parks 0.0483760 0.0
Missouri Retail_Recreation -0.0476974 -36.0
Arizona Parks -0.0475900 -44.5
Kentucky Workplace -0.0472408 -36.0
Missouri Grocery_Pharmacy 0.0471788 2.0
Illinois Grocery_Pharmacy -0.0413057 2.0
Indiana Transit 0.0403144 -29.0
Illinois Retail_Recreation 0.0401067 -40.0
West Virginia Transit -0.0400159 -45.0
Texas Retail_Recreation 0.0380055 -40.0
Florida Transit -0.0376599 -49.0
Colorado Grocery_Pharmacy -0.0359283 -17.0
Nevada Workplace 0.0327453 -40.0
Colorado Retail_Recreation -0.0317055 -44.0
Minnesota Residential -0.0314567 17.0
Ohio Workplace -0.0294480 -35.0
Utah Grocery_Pharmacy 0.0286012 -4.0
Mississippi Parks -0.0280012 -25.0
Tennessee Grocery_Pharmacy 0.0252797 6.0
Oklahoma Retail_Recreation 0.0251635 -31.0
Tennessee Transit -0.0212160 -32.0
Iowa Workplace -0.0205845 -30.0
Alabama Residential -0.0198444 11.0
Wisconsin Retail_Recreation 0.0185339 -44.0
New Mexico Workplace 0.0181485 -34.0
Kansas Residential -0.0175172 13.0
Iowa Residential -0.0141633 13.0
Kansas Retail_Recreation -0.0132294 -37.0
Georgia Transit -0.0129488 -35.0
Maryland Parks -0.0075696 27.0
Vermont Transit 0.0068563 -63.0
Oklahoma Transit 0.0065825 -26.0
Nevada Grocery_Pharmacy 0.0061812 -12.5
Colorado Workplace 0.0054031 -39.0
Tennessee Retail_Recreation -0.0049494 -30.0
Iowa Grocery_Pharmacy 0.0024907 4.0
Arkansas Grocery_Pharmacy 0.0014307 3.0
Alaska Parks NA 29.0
District of Columbia Retail_Recreation NA -69.0
District of Columbia Grocery_Pharmacy NA -28.0
District of Columbia Parks NA -65.0
District of Columbia Transit NA -69.0
District of Columbia Workplace NA -48.0
District of Columbia Residential NA 17.0 
# sanity check
ggplot(filter(plot_data,Province.State %in% c("Pennsylvania","Maryland","New Jersey","California","Delaware","Connecticut")),aes(x=Total_confirmed_cases.per100,fill=variable))+geom_histogram()+
  facet_grid(~Province.State)+
    default_theme+
  theme(legend.position = "bottom")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

write_plot(mobility.plot,wd = results_dir)
## [1] "/Users/stevensmith/Projects/coronavirus/results/mobility.plot.png"
write_plot(mobility.global.plot,wd = results_dir)
## [1] "/Users/stevensmith/Projects/coronavirus/results/mobility.global.plot.png"
(plot_data.permobility_summary.plot<-ggplot(plot_data.permobility_summary,aes(x=variable,y=median_change))+
  geom_jitter(size=2,width=.2)+
  #geom_jitter(data=plot_data.permobility_summary %>% arrange(-abs(median_change)) %>% head(n=15),aes(col=Province.State),size=2,width=.2)+
  default_theme+
  ggtitle("Per-Sate Median Change in Mobility")+
  xlab("Mobility Meaure")+
  ylab("Median Change from Baseline"))

write_plot(plot_data.permobility_summary.plot,wd = results_dir)
## [1] "/Users/stevensmith/Projects/coronavirus/results/plot_data.permobility_summary.plot.png"

DELIVERABLE MANIFEST

The following link to commited documents pushed to github. These are provided as a convienence, but note this is a manual process. The generation of reports, plots and tables is not coupled to the execution of this markdown. ## Report This report, html & pdf

Plots

github_root<-"https://github.com/sbs87/coronavirus/blob/master/"

plot_handle<-c("Corona_Cases.world.long.plot",
               "Corona_Cases.world.loglong.plot",
               "Corona_Cases.world.mortality.plot",
               "Corona_Cases.world.casecor.plot",
               "Corona_Cases.city.long.plot",
               "Corona_Cases.city.loglong.plot",
               "Corona_Cases.city.mortality.plot",
               "Corona_Cases.city.casecor.plot",
               "Corona_Cases.city.long.normalized.plot",
               "Corona_Cases.US_state.lm.plot",
               "Corona_Cases.US_state.summary.plot")


deliverable_manifest<-data.frame(
  name=c("World total & death cases, longitudinal",
         "World log total & death cases, longitudinal",
         "World mortality",
         "World total & death cases, correlation",
         "City total & death cases, longitudinal",
         "City log total & death cases, longitudinal",
         "City mortality",
         "City total & death cases, correlation",
         "City population normalized total & death cases, longitudinal",
         "State total cases (select) with linear model, longitudinal",
         "State total cases, longitudinal"),
  plot_handle=plot_handle,
  link=paste0(github_root,"results/",plot_handle,".png")
)


(tmp<-data.frame(row_out=apply(deliverable_manifest,MARGIN = 1,FUN = function(x) paste(x[1],x[2],x[3],sep=" | "))))
##                                                                                                                                                                                                        row_out
## 1                                           World total & death cases, longitudinal | Corona_Cases.world.long.plot | https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.world.long.plot.png
## 2                                 World log total & death cases, longitudinal | Corona_Cases.world.loglong.plot | https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.world.loglong.plot.png
## 3                                                         World mortality | Corona_Cases.world.mortality.plot | https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.world.mortality.plot.png
## 4                                      World total & death cases, correlation | Corona_Cases.world.casecor.plot | https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.world.casecor.plot.png
## 5                                              City total & death cases, longitudinal | Corona_Cases.city.long.plot | https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.city.long.plot.png
## 6                                    City log total & death cases, longitudinal | Corona_Cases.city.loglong.plot | https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.city.loglong.plot.png
## 7                                                            City mortality | Corona_Cases.city.mortality.plot | https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.city.mortality.plot.png
## 8                                         City total & death cases, correlation | Corona_Cases.city.casecor.plot | https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.city.casecor.plot.png
## 9  City population normalized total & death cases, longitudinal | Corona_Cases.city.long.normalized.plot | https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.city.long.normalized.plot.png
## 10                     State total cases (select) with linear model, longitudinal | Corona_Cases.US_state.lm.plot | https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.US_state.lm.plot.png
## 11                                      State total cases, longitudinal | Corona_Cases.US_state.summary.plot | https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.US_state.summary.plot.png
row_out<-apply(tmp, 2, paste, collapse="\t\n")
name handle link
World total & death cases, longitudinal Corona_Cases.world.long.plot https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.world.long.plot.png
World log total & death cases, longitudinal Corona_Cases.world.loglong.plot https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.world.loglong.plot.png
World mortality Corona_Cases.world.mortality.plot https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.world.mortality.plot.png
World total & death cases, correlation Corona_Cases.world.casecor.plot https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.world.casecor.plot.png
City total & death cases, longitudinal Corona_Cases.city.long.plot https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.city.long.plot.png
City log total & death cases, longitudinal Corona_Cases.city.loglong.plot https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.city.loglong.plot.png
City mortality Corona_Cases.city.mortality.plot https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.city.mortality.plot.png
City total & death cases, correlation Corona_Cases.city.casecor.plot https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.city.casecor.plot.png
City population normalized total & death cases, longitudinal Corona_Cases.city.long.normalized.plot https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.city.long.normalized.plot.png
State total cases (select) with linear model, longitudinal Corona_Cases.US_state.lm.plot https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.US_state.lm.plot.png
State total cases, longitudinal Corona_Cases.US_state.summary.plot https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.US_state.summary.plot.png

Tables

CONCLUSION

Overall, the trends of COVID-19 cases is no longer in log-linear phase for world or U.S. (but some regions like MD are still in the log-linear phase). Mortality rate (deaths/confirmed RNA-based cases) is >1%, with a range depending on region. Mobility is not a strong indicator of caseload (U.S. data).

See table below for detailed breakdown.

Question Answer
What is the effect on social distancing, descreased mobility on case load?
 There is not a strong apparent effect on decreased mobility (work, grocery, retail) or increased mobility (at residence, parks) on number of confirmed cases, either as a country (U.S.) or state level. California appears to have one of the best correlations, but this is a mixed bag
What is the trend in cases, mortality across geopgraphical regions?
 The confirmed total casees and mortality is overall log-linear for most countries, with a trailing off beginning for most (inlcuding U.S.). On the state level, NY, NJ, PA starting to trail off; MD is still in log-linear phase. Mortality and case load are highly correlated for NY, NJ, PA, MD. The mortality rate flucutates for a given region, but is about 3% overall.

END

End: ##—— Wed May 27 21:58:56 2020 ——##

Cheatsheet: http://rmarkdown.rstudio.com>

Sandbox

# Geographical heatmap!
install.packages("maps")
library(maps)
library
mi_counties <- map_data("county", "pennsylvania") %>% 
  select(lon = long, lat, group, id = subregion)
head(mi_counties)

ggplot(mi_counties, aes(lon, lat)) + 
  geom_point(size = .25, show.legend = FALSE) +
  coord_quickmap()
mi_counties$cases<-1:2226
name_overlaps(metadata,Corona_Cases.US_state)

tmp<-merge(Corona_Cases.US_state,metadata)
ggplot(filter(tmp,Province.State=="Pennsylvania"), aes(Long, Lat, group = as.factor(City))) +
  geom_polygon(aes(fill = Total_confirmed_cases), colour = "grey50") + 
  coord_quickmap()


ggplot(Corona_Cases.US_state, aes(Long, Lat))+
  geom_polygon(aes(fill = Total_confirmed_cases ), color = "white")+
  scale_fill_viridis_c(option = "C")
dev.off()


require(maps)
require(viridis)

world_map <- map_data("world")
ggplot(world_map, aes(x = long, y = lat, group = group)) +
  geom_polygon(fill="lightgray", colour = "white")

head(world_map)
head(Corona_Cases.US_state)
unique(select(world_map,c("region","group"))) %>% filter()

some.eu.countries <- c(
  "US"
)
# Retrievethe map data
some.eu.maps <- map_data("world", region = some.eu.countries)

# Compute the centroid as the mean longitude and lattitude
# Used as label coordinate for country's names
region.lab.data <- some.eu.maps %>%
  group_by(region) %>%
  summarise(long = mean(long), lat = mean(lat))

unique(filter(some.eu.maps,subregion %in% Corona_Cases.US_state$Province.State) %>% select(subregion))
unique(Corona_Cases.US_state$Total_confirmed_cases.log)
ggplot(filter(Corona_Cases.US_state,Date=="2020-04-17") aes(x = Long, y = Lat)) +
  geom_polygon(aes( fill = Total_confirmed_cases.log))+
  #geom_text(aes(label = region), data = region.lab.data,  size = 3, hjust = 0.5)+
  #scale_fill_viridis_d()+
  #theme_void()+
  theme(legend.position = "none")
library("sf")
library("rnaturalearth")
library("rnaturalearthdata")

world <- ne_countries(scale = "medium", returnclass = "sf")
class(world)
ggplot(data = world) +
    geom_sf()

counties <- st_as_sf(map("county", plot = FALSE, fill = TRUE))
counties <- subset(counties, grepl("florida", counties$ID))
counties$area <- as.numeric(st_area(counties))
#install.packages("lwgeom")
class(counties)
head(counties)
ggplot(data = world) +
    geom_sf(data=Corona_Cases.US_state) +
    #geom_sf(data = counties, aes(fill = area)) +
  geom_sf(data = counties, aes(fill = area)) +
   # scale_fill_viridis_c(trans = "sqrt", alpha = .4) +
    coord_sf(xlim = c(-88, -78), ylim = c(24.5, 33), expand = FALSE)


head(counties)
tmp<-unique(select(filter(Corona_Cases.US_state,Date=="2020-04-17"),c(Lat,Long,Total_confirmed_cases.per100)))
st_as_sf(map("county", plot = FALSE, fill = TRUE))

join::inner_join.sf(Corona_Cases.US_state, counties)

library(sf)
library(sp)

nc <- st_read(system.file("shape/nc.shp", package="sf"))
class(nc)


spdf <- SpatialPointsDataFrame(coords = select(Corona_Cases.US_state,c("Lat","Long")), data = Corona_Cases.US_state,
                               proj4string = CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"))

head(spdf)
class(spdf)
st_cast(spdf)

filter(Corona_Cases.US_state.summary,Date=="2020-04-20" & Province.State %in% top_states_modified)
id

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